tbuckley/cabot-search huggingface dataset:
Auxiliary use of ChatGPT in surgical diagnosis and treatment – correspondence
Authors: Qing-xin Yu, De-chao Feng, Rui-cheng Wu, Deng-xiong Li
Journal: International Journal of Surgery (2023)

aDepartment of pathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo City, Zhejiang Province, 315211, China bDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, 610041, China Both authors contributed equally to this work and are listed as co-first authors. Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. Published online ■ ■ *Corresponding author. Address: Department of pathology, Ningbo Clinical Pathology Diagnosis center, Ningbo City, Zhejiang Province, China Tel: +86-18313736646. E-mail address: [email protected] (Q. X. Yu) *Corresponding author. Address: Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu City, Sichuan Province, China. Tel: +86-28-85422444 Fax: +86-28-85422451. E-mail address: [email protected] (D. X. Li) This is an open access article distributed under the Creative Commons Attribution-ShareAlike License 4.0, which allows others to remix, tweak, and build upon the work, even for commercial purposes, as long as the author is credited and the new creations are licensed under the identical terms. http://creativecommons.org/licenses/by-sa/4.0/
Revolutionizing clinical trials: the role of AI in accelerating medical breakthroughs
Authors: Hitesh Chopra, None Annu, Dong K. Shin, Kavita Munjal, None Priyanka, Kuldeep Dhama, Talha B. Emran
Journal: International Journal of Surgery (2023)

Clinical trials are the essential assessment for safe, reliable, and effective drug development. Data-related limitations, extensive manual efforts, remote patient monitoring, and the complexity of traditional clinical trials on patients drive the application of Artificial Intelligence (AI) in medical and healthcare organisations. For expeditious and streamlined clinical trials, a personalised AI solution is the best utilisation. AI provides broad utility options through structured, standardised, and digitally driven elements in medical research. The clinical trials are a time-consuming process with patient recruitment, enrolment, frequent monitoring, and medical adherence and retention. With an AI-powered tool, the automated data can be generated and managed for the trial lifecycle with all the records of the medical history of the patient as patient-centric AI. AI can intelligently interpret the data, feed downstream systems, and automatically fill out the required analysis report. This article explains how AI has revolutionised innovative ways of collecting data, biosimulation, and early disease diagnosis for clinical trials and overcomes the challenges more precisely through cost and time reduction, improved efficiency, and improved drug development research with less need for rework. The future implications of AI to accelerate clinical trials are important in medical research because of its fast output and overall utility.
scIBD: a self-supervised iterative-optimizing model for boosting the detection of heterotypic doublets in single-cell chromatin accessibility data
Authors: Wenhao Zhang, Rui Jiang, Shengquan Chen, Ying Wang
Journal: Genome biology (2023)

Abstract Application of the widely used droplet-based microfluidic technologies in single-cell sequencing often yields doublets, introducing bias to downstream analyses. Especially, doublet-detection methods for single-cell chromatin accessibility sequencing (scCAS) data have multiple assay-specific challenges. Therefore, we propose scIBD, a self-supervised iterative-optimizing model for boosting heterotypic doublet detection in scCAS data. scIBD introduces an adaptive strategy to simulate high-confident heterotypic doublets and self-supervise for doublet-detection in an iteratively optimizing manner. Comprehensive benchmarking on various simulated and real datasets demonstrates the outperformance and robustness of scIBD. Moreover, the downstream biological analyses suggest the efficacy of doublet-removal by scIBD.
Progressing nanotechnology to improve targeted cancer treatment: overcoming hurdles in its clinical implementation
Authors: Mohammad Chehelgerdi, Matin Chehelgerdi, Omer Qutaiba B. Allela, Renzon Daniel Cosme Pecho, Narayanan Jayasankar, Devendra Pratap Rao, Tamilanban Thamaraikani, Manimaran Vasanthan, Patrik Viktor, Natrayan Lakshmaiya, Mohamed J. Saadh, Ayesha Amajd, Mabrouk A. Abo-Zaid, Roxana Yolanda Castillo-Acobo, Ahmed H. Ismail, Ali H. Amin, Reza Akhavan-Sigari
Journal: Molecular Cancer (2023)

Abstract The use of nanotechnology has the potential to revolutionize the detection and treatment of cancer. Developments in protein engineering and materials science have led to the emergence of new nanoscale targeting techniques, which offer renewed hope for cancer patients. While several nanocarriers for medicinal purposes have been approved for human trials, only a few have been authorized for clinical use in targeting cancer cells. In this review, we analyze some of the authorized formulations and discuss the challenges of translating findings from the lab to the clinic. This study highlights the various nanocarriers and compounds that can be used for selective tumor targeting and the inherent difficulties in cancer therapy. Nanotechnology provides a promising platform for improving cancer detection and treatment in the future, but further research is needed to overcome the current limitations in clinical translation. Graphical Abstract
Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction
Authors: Markus Müller, Florian Huber, Marion Arnaud, Anne I. Kraemer, Emma Ricart Altimiras, Justine Michaux, Marie Taillandier-Coindard, Johanna Chiffelle, Baptiste Murgues, Talita Gehret, Aymeric Auger, Brian J. Stevenson, George Coukos, Alexandre Harari, Michal Bassani-Sternberg
Journal: Immunity (2023)

The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.
Author Correction: Vepafestinib is a pharmacologically advanced RET-selective inhibitor with high CNS penetration and inhibitory activity against RET solvent front mutations
Authors: Isao Miyazaki, Igor Odintsov, Keiji Ishida, Allan J. W. Lui, Masanori Kato, Tatsuya Suzuki, Tom Zhang, Kentaro Wakayama, Renate I. Kurth, Ryan Cheng, Hidenori Fujita, Lukas Delasos, Morana Vojnic, Inna Khodos, Yukari Yamada, Kota Ishizawa, Marissa S. Mattar, Kaoru Funabashi, Qing Chang, Shuichi Ohkubo, Wakako Yano, Ryuichiro Terada, Claudio Giuliano, Yue Christine Lu, Annalisa Bonifacio, Siddharth Kunte, Monika A. Davare, Emily H. Cheng, Elisa de Stanchina, Emanuela Lovati, Yoshikazu Iwasawa, Marc Ladanyi, Romel Somwar
Journal: Nature Cancer (2023)
Intravitreal injection of a rationally designed AAV capsid library in non-human primate identifies variants with enhanced retinal transduction and neutralizing antibody evasion
Authors: Patrick C Kellish, Damien Marsic, Sean M Crosson, Shreyasi Choudhury, Miranda L Scalabrino, Christianne E Strang, Julie Hill, K Tyler McCullough, James J Peterson, Diego Fajardo, Siddhant Gupte, Victoria Makal, Oleksandr Kondratov, Liudmyla Kondratova, Siva Iyer, C Douglas Witherspoon, Paul D Gamlin, Sergei Zolotukhin, Sanford L Boye, Shannon E Boye
Journal: Molecular Therapy (2023)

Adeno-associated virus (AAV) continues to be the gold standard vector for therapeutic gene delivery and has proven especially useful for treating ocular disease. Intravitreal injection (IVtI) is a promising delivery route because it increases accessibility of gene therapies to larger patient populations. However, data from clinical and non-human primate (NHP) studies utilizing currently available capsids indicate that anatomical barriers to AAV and pre-existing neutralizing antibodies can restrict gene expression to levels that are "sub-therapeutic" in a substantial proportion of patients. Here, we performed a combination of directed evolution in NHPs of an AAV2-based capsid library with simultaneous mutations across six surface-exposed variable regions and rational design to identify novel capsid variants with improved retinal transduction following IVtI. Following two rounds of screening in NHP, enriched variants were characterized in intravitreally injected mice and NHPs and shown to have increased transduction relative to AAV2. Lead capsid variant, P2-V1, demonstrated an increased ability to evade neutralizing antibodies in human vitreous samples relative to AAV2 and AAV2.7m8. Taken together, this study further contributed to our understanding of the selective pressures associated with retinal transduction via the vitreous and identified promising novel AAV capsid variants for clinical consideration.
Semaglutide in Patients With Obesity and Heart Failure Across Mildly Reduced or Preserved Ejection Fraction
Authors: Javed Butler, Steen Z. Abildstrøm, Barry A. Borlaug, Melanie J. Davies, Dalane W. Kitzman, Mark C. Petrie, Sanjiv J. Shah, Subodh Verma, Walter P. Abhayaratna, Vijay Chopra, Justin A. Ezekowitz, Michael Fu, Hiroshi Ito, Małgorzata Lelonek, Julio Núñez, Eduardo Perna, Morten Schou, Michele Senni, Peter van der Meer, Dirk von Lewinski, Dennis Wolf, Rebecca L. Altschul, Søren Rasmussen, Mikhail N. Kosiborod
Journal: Journal of the American College of Cardiology (2023)

Many therapies for heart failure (HF) have shown differential impact across the spectrum of left ventricular ejection fraction (LVEF).
α-HBDH is a Superior to LDH in Predicting Cardiac Injury in Patients with Acute Aortic Dissection
Authors: Yong-bo Zhao, Dong Ma, Xue-Feng Wang, Yong-bo Zhao, Yue Sun, Yun-Jing Zhang
Journal: International Journal of Surgery (2023)

Objective: Acute aortic dissection (AAD) has a high rate of mortality and postoperative complications, but fewer indicators can predict postoperative cardiac injury complications. This study aimed to investigate whether α-HBDH is superior to LDH as a predictor of in-hospital mortality and postoperative cardiac injury in patients with AAD. Methods: In this retrospective study, a total of 369 patients from 2015 to 2021 were enrolled and divided into three groups (T1: low, T2: medium and T3: high) based on the tertiles of α-HBDH levels on admission. The relationship between α-HBDH and AAD was determined by correlation analysis and logistic regression model, with the consolidation using Kaplan-Meier and restricted cubic spline (RCS) analysis for predicting the in-hospital death and cardiac injury complications. Last, the relationship between α-HBDH levels and in-hospital mortality and cardiac injury was further verified through subgroup analysis. Results: In the logistic regression model, α-HBDH in the high-level group was 9.869 times that in the low-level group [OR (95CI): 9.869 (2.148-45.349), P=0.003], while this feature was not observed in LDH [OR (95CI): 1.437 (0.342-6.034), P=0.621]. Kaplan-Meier analysis showed that increased α-HBDH levels within 30 days were associated with poor survival within 30 days in AAD patients (log rank test, P&lt;0.01), especially in acute stanford A dissection. RCS showed that 204 U/L was the optimal cut-off value of α-HBDH for in-hospital mortality and postoperative cardiac injury, which facilitated clinical stratification of patients with AAD. Subgroup analysis confirmed a stable correlation between α-HBDH level and hospital mortality and cardiac injury (P&gt;0.05). Conclusion: α-HBDH would be better than LDH in predicting the in-hospital death and postoperative cardiac injury, guiding admission stratification of patients with AAD.
Harnessing the power of synthetic data in healthcare: innovation, application, and privacy
Authors: Mauro Giuffrè, Dennis L. Shung
Journal: npj Digital Medicine (2023)

Abstract Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use.
TDP-43 as a therapeutic target in neurodegenerative diseases: Focusing on motor neuron disease and frontotemporal dementia
Authors: Afshin Babazadeh, Stephanie L. Rayner, Albert Lee, Roger S. Chung
Journal: Ageing Research Reviews (2023)

A common feature of adult-onset neurodegenerative diseases is the presence of characteristic pathological accumulations of specific proteins. These pathological protein depositions can vary in their protein composition, cell-type distribution, and intracellular (or extracellular) location. For example, abnormal cytoplasmic protein deposits which consist of the TDP-43 protein are found within motor neurons in patients with amyotrophic lateral sclerosis (ALS, a common form of motor neuron disease) and frontotemporal dementia (FTD). The presence of these insoluble intracellular TDP-43 inclusions suggests that restoring TDP-43 homeostasis represents a potential therapeutical strategy, which has been demonstrated in alleviating neurodegenerative symptoms in cell and animal models of ALS/FTD. We have reviewed the mechanisms that lead to disrupted TDP-43 homeostasis and discussed how small molecule-based therapies could be applied in modulating these mechanisms. This review covers recent advancements and challenges in small molecule-based therapies that could be used to clear pathological forms of TDP-43 through various protein homeostasis mechanisms and advance the way towards finding effective therapeutical drug discoveries for neurodegenerative diseases characterized by TDP-43 proteinopathies, especially ALS and FTD. We also consider the wider insight of these therapeutic strategies for other neurodegenerative diseases.
A hand gesture recognition strategy based on virtual dimension increase of EMG
Authors: Yuxuan Wang, Ye Tian, Jinying Zhu, Haotian She, Yinlai Jiang, Zhihong Jiang, Hiroshi Yokoi
Journal: Cyborg and Bionic Systems (2023)

The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition. However, as the number of acquisition channels increases, its effect on the improvement of the accuracy of gesture recognition gradually diminishes, resulting in the improvement of the control effect reaching a plateau. To address these problems, this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels. This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels, ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings. Meanwhile, based on the idea of the filtered feature selection method, a quantitative measure of sample sets (separability of feature vectors [SFV]) derived from the divergence and correlation of the extracted features is introduced. The SFV value can predict the classification effect before performing the classification, and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change. Compared to the statistical motion intention recognition success rate, SFV is a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.
Author Correction: Post-translational modifications of soluble α-synuclein regulate the amplification of pathological α-synuclein
Authors: Shujing Zhang, Ruowei Zhu, Buyan Pan, Hong Xu, Modupe F. Olufemi, Ronald J. Gathagan, Yuanxi Li, Luyan Zhang, Jasmine Zhang, Wenxuan Xiang, Eliot Masahiro Kagan, Xingjun Cao, Chaoxing Yuan, Soo-Jung Kim, Christopher K. Williams, Shino Magaki, Harry V. Vinters, Hilal A. Lashuel, Benjamin A. Garcia, E. James Petersson, John Q. Trojanowski, Virginia M.-Y. Lee, Chao Peng
Journal: Nature Neuroscience (2023)
E7820, an anti-cancer sulfonamide, degrades RBM39 in patients with splicing factor mutant myeloid malignancies: a phase II clinical trial
Authors: Jan Philipp Bewersdorf, Maximilian Stahl, Justin Taylor, Xiaoli Mi, Namrata Sonia Chandhok, Justin Watts, Andriy Derkach, Mateusz Wysocki, Sydney X. Lu, Jessie Bourcier, Simon J. Hogg, Jahan Rahman, Sana Chaudhry, Tulasigeri M. Totiger, Omar Abdel-Wahab, Eytan M. Stein
Journal: Leukemia (2023)
Publisher Correction: Axicabtagene ciloleucel as second-line therapy in large B cell lymphoma ineligible for autologous stem cell transplantation: a phase 2 trial
Authors: Roch Houot, Emmanuel Bachy, Guillaume Cartron, François-Xavier Gros, Franck Morschhauser, Lucie Oberic, Thomas Gastinne, Pierre Feugier, Rémy Duléry, Catherine Thieblemont, Magalie Joris, Fabrice Jardin, Sylvain Choquet, Olivier Casasnovas, Gabriel Brisou, Morgane Cheminant, Jacques-Olivier Bay, Francisco Llamas Gutierrez, Cédric Menard, Karin Tarte, Marie-Hélène Delfau, Cédric Portugues, Emmanuel Itti, Xavier Palard-Novello, Paul Blanc-Durand, Yassine Al Tabaa, Clément Bailly, Camille Laurent, François Lemonnier
Journal: Nature Medicine (2023)

In the version of this article initially published, the title of the paper, now reading "Axicabtagene ciloleucel as second-line therapy in large B cell lymphoma ineligible for autologous stem cell transplantation: a phase 2 trial," appeared incorrectly as "Axicabtagene ciloleucel in large B cell lymphoma ineligible for autologous stem cell transplantation: the phase 2 ALYCANTE trial.
Early versus delayed antihypertensive treatment in patients with acute ischaemic stroke: multicentre, open label, randomised, controlled trial
Authors: Liping Liu, Xuewei Xie, Yuesong Pan, Aili Wang, Yufei Wei, Jingyi Liu, Ximing Nie, Dacheng Liu, Zilin Zhao, Penglian Wang, Suwen Shen, Chongke Zhong, Tan Xu, Dali Wang, Gui-Chun Wang, Denghua Song, Yunsheng Ma, Jinguo Zhao, Yong Jiang, Jing Jing, Xia Meng, Katherine Obst, Chung-Shiuan Chen, David Wang, Yilong Wang, Yonghong Zhang, Yongjun Wang, Jiang He
Journal: BMJ (2023)

To compared the effect of early antihypertensive treatment started within 24-48 h of stroke onset versus delaying treatment until day eight on reducing dependency or death.Multicentre, randomised, open label trial.106 hospitals in China between 13 June 2018 and 10 July 2022.4810 patients (≥40 years) were enrolled with acute ischaemic stroke within 24-48 h of symptom onset and elevated systolic blood pressure between 140 mm Hg and <220 mm Hg.Patients were randomly assigned to receive antihypertensive treatment immediately after randomisation (aimed at reducing systolic blood pressure by 10%-20% within the first 24 h and a mean blood pressure <140/90 mm Hg within seven days) or to discontinue antihypertensive medications for seven days if they were taking them, and then receive treatment on day 8 (aimed at achieving mean blood pressure <140/90 mm Hg).The primary outcome was the combination of functional dependency or death (modified Rankin scale score ≥3) at 90 days. Intention to treat analyses were conducted.2413 patients were assigned to the early treatment group and 2397 were assigned to the delayed treatment group. Mean systolic blood pressure was reduced by 9.7% (from 162.9 mm Hg to 146.4 mm Hg) in the early treatment group and by 4.9% (from 162.8 mm Hg to 154.3 mm Hg) in the delayed treatment group within 24 h after randomisation (P for group difference <0.001). Mean systolic blood pressure was 139.1 mm Hg in the early treatment group and 150.9 mm Hg in the delayed treatment group on day seven (P for group difference <0.001). Additionally, 54.6% of patients in the early treatment group and 22.4% in the delayed treatment group had blood pressure of less than 140/90 mm Hg (P<0.001 for group difference) on day seven. At day 90, 289 trial participants (12.0%) in the early treatment group, compared with 250 (10.5%) in the delayed treatment group, had died or experienced a dependency (odds ratio 1.18 (95% confidence interval 0.98 to 1.41), P=0.08). No significant differences in recurrent stroke or adverse events were reported between the two groups.Among patients with mild-to-moderate acute ischaemic stroke and systolic blood pressure between 140 mm Hg and <220 mm Hg who did not receive intravenous thrombolytic treatment, early antihypertensive treatment did not reduce the odds of dependency or death at 90 days.ClinicalTrials.gov Identifier NCT03479554.
Generative-AI-Generated Challenges for Health Data Research
Authors: Kayte Spector-Bagdady
Journal: The American Journal of Bioethics (2023)

Click to increase image sizeClick to decrease image sizeThis article refers to:What Should ChatGPT Mean for Bioethics?Is Academic Enhancement Possible by Means of Generative AI-Based Digital Twins?Ethics Education for Healthcare Professionals in the Era of ChatGPT and Other Large Language Models: Do We Still Need It?"Large Language Models" Do Much More than Just Language: Some Bioethical Implications of Multi-Modal AIThe Epistemological Danger of Large Language ModelsAUTOGEN: A Personalized Large Language Model for Academic Enhancement—Ethics and Proof of PrincipleUnreliable LLM Bioethics Assistants: Ethical and Pedagogical RisksGenerative AI, Specific Moral Values: A Closer Look at ChatGPT's New Ethical Implications for Medical AIThe Hidden Costs of ChatGPT: A Call for Greater Transparency DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Human Genome Research Institute (K01HG01049), the National Center for Advancing Translational Sciences (R01TR004244, UM1TR004404), and the Center for Bioethics & Social Sciences in Medicine at the University of Michigan Medical School.